Community detection in node-attributed social networks: a survey
P Chunaev - Computer Science Review, 2020 - Elsevier
Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …
[HTML][HTML] A novel nonnegative matrix factorization-based model for attributed graph clustering by incorporating complementary information
Attributed graph clustering is a prominent research area, catering to the increasing need for
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …
Graph regularized nonnegative matrix factorization for community detection in attributed networks
K Berahmand, M Mohammadi… - … on Network Science …, 2022 - ieeexplore.ieee.org
Community detection has become an important research topic in machine learning due to
the proliferation of network data. However, most existing methods have been developed …
the proliferation of network data. However, most existing methods have been developed …
A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix
The most basic and significant issue in complex network analysis is community detection,
which is a branch of machine learning. Most current community detection approaches, only …
which is a branch of machine learning. Most current community detection approaches, only …
WSNMF: Weighted symmetric nonnegative matrix factorization for attributed graph clustering
Abstract In recent times, Symmetric Nonnegative Matrix Factorization (SNMF), a derivative of
Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph …
Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph …
DAC-HPP: deep attributed clustering with high-order proximity preserve
Attributed graph clustering, the task of grouping nodes into communities using both graph
structure and node attributes, is a fundamental problem in graph analysis. Recent …
structure and node attributes, is a fundamental problem in graph analysis. Recent …
Community detection on large complex attribute network
A large payment network contains millions of merchants and billions of transactions, and the
merchants are described in a large number of attributes with incomplete values …
merchants are described in a large number of attributes with incomplete values …
Predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach
Infrastructures are interdependent systems and their interdependency can influence their
resilience to routine failures and extreme events. Even though infrastructure resilience has …
resilience to routine failures and extreme events. Even though infrastructure resilience has …
Inductive representation learning via CNN for partially-unseen attributed networks
Network embedding aims to map a complex network into a low-dimensional vector space
while maximally preserving the properties of the original network. An attributed network is a …
while maximally preserving the properties of the original network. An attributed network is a …
Multiobjective optimization and local merge for clustering attributed graphs
C Pizzuti, A Socievole - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
Methods for detecting the community structure in complex networks have mainly focused on
network topology, neglecting the rich content information often associated with nodes. In the …
network topology, neglecting the rich content information often associated with nodes. In the …